How Do I Select IP to Use for My Machine Learning System? 

  • Helena Zheng, Arm

Machine learning (ML) processing requirements vary significantly according to the network and workload; there is no “one-size-fits-all” solution. Examining use cases, workloads, and performance data from real networks, this talk will give examples to help you choose the right Project Trillium IP from Arm for your application. Examples will include MCUs for cost- and power-constrained embedded IoT systems through CPUs for moderate performance with general-purpose programmability. Other examples include GPUs for faster performance with graphics-intensive applications to NPUs, such as with the Arm ML processor, for intensive ML processing, giving the highest available performance and efficiency.   

  • Date:Tuesday, October 16
  • Time:4:30 PM - 5:20 PM
  • Location:Executive Ballroom 210G
  • Session Type:Conference Session
  • Room:Executive Ballroom 210G
  • Pass Type:All-Access Pass
  • Secondary Track:High-Efficiency Systems